PHM Integration with Maintenance and Inventory Management Systems

Tu, Fang; Ghoshal, Sudipto; Luo, Jie; Biswas, Gautam; Mahadevan, Sankaran; Jaw, Link C.; Navarra, Kelly R.

doi:10.1109/aero.2007.352918

Cited by 24 publications

(19 citation statements)

References 14 publications

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“…These functions are parameterized by a set of unknown wear parameters, forming the unknown parameter vector θ(t). Impeller wear is represented as a decrease in impeller area A [7,9]. We use the erosive wear equation [12].…”

Section: Damage Modelingmentioning

confidence: 99%

“…A principal component analysis method is applied for condition monitoring of a pump using vibration signals in [8]. A model-based approach is presented in [9], however it considers only a single degradation mode. We illustrate here our model-based prognostic approach for centrifugal pumps using a number of simulation-based experiments when multiple damage mechanisms are active.…”

Section: Introductionmentioning

confidence: 99%

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Multiple damage progression paths in model-based prognostics

Daigle

Goebel

2011

2011 Aerospace Conference

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Abstract-Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our modelbased prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.

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Section: Damage Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple damage progression paths in model-based prognostics

Daigle

Goebel

2011

2011 Aerospace Conference

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“…Some of the published researches using these techniques can be found in references [2][3][4][5][6]. Even though the accuracy of these techniques is reasonably high, they are only suitable for specific components and each component requires a specific mathematical model.…”

Section: Introductionmentioning

confidence: 99%

A Classification and Regression Trees (CART) Model of Parallel Structure and Long-term Prediction Prognosis of Machine Condition

Tran

Yang²,

Tan

2009

Structural Health Monitoring

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This paper presents a combined prediction model involving the parallel of classification and regression trees (CART) model, namely p-CART, and a long-term direct prediction methodology of time series techniques to predict the future stages of the machine's operating conditions. p-CART model consists of multiple CART models which are connected in parallel. Each submodel in the p-CART is trained independently. Based on the observations, these sub-models are subsequently used to predict the future values of the machine's operating conditions separately with the same embedding dimension but the different observations' indices. Finally, the predicted results of sub-models are combined to produce the final results of the predicting process. Real trending data acquired from condition monitoring routine of low methane compressor are employed for evaluating the proposed method. A comparative study of the predicted results obtained from traditional CART and p-CART models is also carried out to appraise the prediction capability of proposed model. In addition, a further improvement in predicting capability of p-CART is proposed to ameliorate the accuracy and efficiency of this method.

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“…These approaches use residuals as features, which are the outcomes of consistency checks between the sensed measurements of system and the outputs of a mathematical model [4]. Some of the published researches using these approaches can be found in references [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

Tran

Yang

Tan

2009

Expert Systems with Applications

101

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This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines' operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

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PHM Integration with Maintenance and Inventory Management Systems

Cited by 24 publications

References 14 publications

Multiple damage progression paths in model-based prognostics

Multiple damage progression paths in model-based prognostics

A Classification and Regression Trees (CART) Model of Parallel Structure and Long-term Prediction Prognosis of Machine Condition

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

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